Artificial Intelligence - The New Black?

Even though RPA is only now starting to mature, adding Artificial Intelligence to it will allow the creation of a true digital workforce that will automate activities typically characteristic to knowledge workers. An application such as Intelligent Process Automationconverges AI, RPA and other cognitive tools to enhance productivity. The quality of work will provide service continuity and availability and will reduce costs. AI will be responsible for the “thinking” component of the digital workforce. It will create the new rules for machine learning and will perform supervised learning, reinforcement learning, and unsupervised learning. AI will trigger RPA, which will be handle workflows and APIs, computer command scripts and mechanical robotics.

Artificial Intelligence - The New Black?

1.
AI-the new black?
The final frontier of
productivity
Artificial Intelligence appears to be the new
holy grail or the “new black” of automation,
as it was described by UiPath’s Chief
Robotics Officer, Boris Krumrey, at the 8th
NASSCOM GIC Conclave 2017.
The quest for defining digital labor has been
gaining speed during the past couple of
years and integrating AI and cognitive
capabilities to an RPA platform is the next
step in the quest for increasing the
productivity, agility, and efficiency of human
workers. Businesses have already tapped
into the immense potential that RPA has to
offer, but adopting and integrating
something as revolutionary as AI is a
completely different story.
This paper aims to identify how AI-powered
digital labor could be implemented, what
the main challenges are, and how both
companies and individuals can properly
prepare to ensure these technologies are
successfully deployed.

2.
DIGITAL LABOR EXPLAINED
Automation is not a modern concept.
Historically, it has been a part of the
society for thousands of years, being first
mentioned in Homer’s Iliad. Its main
meaning, that of using tools to increase
human power and productivity, has not
changed dramatically from then to now.
The difference consists of the way in
which these “tools” work: it all started
from those powered by humans (the
wheel, the lever, etc.), to those powered
by natural elements (waterwheels,
windmills, etc.) to the machines of the
Industrial Revolution (steam engines,
mechanical machines etc.). Now, we are
at a stage where automation is coded in
software robots that perform repetitive,
rule-based tasks through a technology
called Robotic Process Automation
(RPA).
Automation is constantly evolving and
does not stop at RPA. Artificial Intelligence
could potentially be a key element in
creating digital labor. It has already
started to be integrated into areas such
as machine learning, cognitive and
computer vision, conversational
technologies, human-to-machine user
interfaces, predictive data analytics, RPA,
cybersecurity, IoT or intelligent
monitoring. Nevertheless, its effects are
not as invasive as they might potentially
be in the future.
Even though RPA is only now starting to
mature, adding AI to it will allow the
creation of a true digital workforce that
will automate activities typically
characteristic to knowledge workers. An
application such as Intelligent Process
Automation converges AI, RPA and other
cognitive tools to enhance productivity.
The quality of work will provide service
continuity and availability, and will reduce
costs. AI will be responsible for the
“thinking” component of the digital
workforce. It will create the new rules for
machine learning and will perform
supervised learning, reinforcement
learning, and unsupervised learning. AI
will trigger RPA, which will be handle
workflows and APIs, computer command
scripts and mechanical robotics.
Thus, RPA will embody the component
that “acts”. Other elements such as
computer vision, unstructured data or
data models will embody the “sensing”
component. All this data will be then fed
into the AI, closing the circle, as illustrated
in the image on the following page.
Embedded automation or inbuilt AI are
technologies that follow a structure
similar to the one described above. These
could work in both attended and
unattended modes.
The key aspect is that AI cannot function
on its own. It would be like a brain without
the arms or the legs. To deliver at its full
potential, AI needs RPA.
AI-the new black? The final frontier of productivity - 2

3.
Nevertheless, we are still far from
achieving fully fledged digital labor. Now,
Artificial Intelligence acts as an smart
tool that acquires new skills, but without
providing a lot of change. In one to two
years, AI will be in a more advanced
stage and will act as a cognitive
assistant, interacting with and servicing
humans, and processing unstructured
data. In three to five years, AI will have
an identity. It will gain true self-learning
capabilities and will be fully adaptable
to perform reliably specific tasks. In
terms of autonomy, it will be
independent at a level permitted by a
human.
AI IMPLEMENTATION CHALLENGES
There is an actual paradox in terms of
trusting and controlling an AI solution.
There is a constant struggle between
retaining control of the AI and trusting its
autonomy because humans want it to
be highly predictable.
An employee can be trusted to perform
as expected by trying to train him or her
to deliver results as a robot and by
doing personal performance controls.
But how do humans trust a robot or any
automated process, for that matter, to
perform as expected?
AI-the new black? The final frontier of productivity - 3

4.
AI-the new black? The final frontier of productivity - 4
For one, there is a difference between an
RPA robot, as one of those developed by
UiPath, and a robot as an AI solution.
While the RPA robot is programmed to
perform certain tasks, the AI solution is
trained by observation. It is no longer a
question of trusting an algorithm, but of
trusting a machine. Treating the AI
solution as a machine that acts as a
human to react intelligently to exceptions
or unexpected changes is key. Trusting AI
machines will take time and requires
transparency and analytics right from the
implementation get go. Finding a
transparent method that helps humans
observe how the machines learn and use
the knowledge the correct way and take
the right decisions will be no easy feat.
Besides trust, another challenge to take
into consideration when implementing
Artificial Intelligence is devising the best
reward system. There are three possible
solutions for this issue: giving up the
reward system—which would require a lot
of data and time, creating a process-
specific reward system or implementing
a human supervision system.
On the one hand, there isn’t any general
reward system that can be applied to
any situation. There are general rules, but
the reward system needs to be very
specific to the process it is applied to.
The process-specific reward system
could potentially allow the AI system self-
train within 48 hours. On the other hand,
with human supervision, the system will
need a lot more time until it learns how to
act. Next, there is the matter of properly
implementing AI to make it efficient.
Understanding that preparation and time
are fundamental to successfully
implementing AI is key.
In terms of doing a business and process
analysis, there needs to be an
understanding of what processes can be
automated using AI and RPA, because
not all of them are suited to be
automated.
Then, enterprises must set up target
operating models that define the
organizational change, implementation
and communications plans that allow
engaging with the key subject matter
experts. They have to devise governance
programs, manage communication to
key stakeholders, define the right working
methods, and track progress and
transformation KPIs.
Next, organizations must define and
implement the technology integration
and infrastructure provision plans, as well
as the robotic user authorizations. Finally,
setting up Centers of Excellence (CoEs) in
charge of implementing and managing
automation as quickly and as efficiently
as possible is also essential.

5.
AI-the new black? The final frontier of productivity - 5
AI OPERATING CHALLENGES
Organizations need to realize that fear of
change and lack of personal employee
motivation are real and can impact
adopting automation successfully.
Artificial Intelligence must not be viewed
as a threat or as taking away work, but as
a means for being more productive and
for tapping new capabilities.
Other major risks reside in operating AI.
Training can either take too long or can
be too complex, and there is the fear that
the technology cannot be trusted and
controlled, as well as not knowing the full
impact of AI taking the wrong decision.
Furthermore, AI capabilities can
potentially be too narrow or too
specialized to cover the full complexity of
a human knowledge employee.
Other challenges reside in defining who is
responsible, from a legal standpoint,
when AI causes damage due to bad
decision-making and integrating AI in
countries that, from a cultural standpoint,
are less risk-averse or that view AI as a
threat to their social status.
Finally, but just as important, a proper
training of the AI system takes time and
responsibility. Organizations must keep
an eye on value misalignment.

6.
AI-the new black? The final frontier of productivity - 6
Training the AI system common sense to
prevent it from taking fatal decisions is
ultimately detrimental.
As seen in the previous illustration,
operating Artificial Intelligence could
function with the collaboration between
the in-house Center of Excellence, AI and
robotics consultants, developers and
users.
The CoE records and defines new
activities and sends them to the
consultants or developers, while it also
agrees to the design change with users
and provides support.
Consultants or developers create,
change and test the platform’s functions
and integrations, perform a joint
functional test and maintain the AI
system platform. The users specify the
process changes or the knowledge items
and then verify them.
A FEW FINAL WORDS…
A mix of AI and RPA, along with a full
cooperation with humans is inevitable.
But the organization must not take its
success for granted. To fully benefit from
the limitless potential of the future digital
labor, enterprises need to grant its
development and adoption the right
amount of time, attention, analysis,
preparation and thought.
If the Human-to-Machine conundrum is
solved, applying AI to countless business
processes will become simpler and
faster, and it will be easier to accelerate
and automate the transformation
process.